Object-based land cover classification of cork oak woodlands using UAV imagery and Orfeo Toolbox

Remote Sensing

Volumn 11, Issue 10, 2019, Pages

  De Luca, Giandomenico ^a   Silva, João M N ^b   Cerasoli, Sofia ^b   Araújo, João ^c   Campos, José ^c   Di Fazio, Salvatore ^a   Modica, Giuseppe ^a  

^a UNIVERSITY MEDITERRANEA OF REGGIO CALABRIA   (Italy)

^b INSTITUTO SUPERIOR DE AGRONOMIA   (Portugal)

^c Spin Works Lda   (Portugal)

Author keywords

Accuracy assessment; Cork oak woodlands; Geographic Object Based Image Analysis (GEOBIA); Land cover classification; Machine learning algorithms; Orfeo ToolBox (OTB); Random Forest (RF); Spectral separability; Support Vector Machines (SVM); Unmanned Aerial Vehicles (UAVs)

Indexed keywords

ANTENNAS; DATA MINING; DECISION TREES; ECOSYSTEMS; FIXED WINGS; FORESTRY; IMAGE CLASSIFICATION; IMAGE SEGMENTATION; LEARNING SYSTEMS; MACHINE LEARNING; SUPPORT VECTOR MACHINES; SURVEYS; UNMANNED AERIAL VEHICLES (UAV);

ACCURACY ASSESSMENT; CORK OAK; GEOGRAPHIC OBJECT-BASED IMAGE ANALYSIS; LAND COVER CLASSIFICATION; ORFEO TOOLBOX (OTB); RANDOM FORESTS; SPECTRAL SEPARABILITY;

LEARNING ALGORITHMS;

EID: 85066755628     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs11101238     Document Type: Article

References (63)

1. Modica, G.; Solano, F.; Merlino, A.; di Fazio, S.; Barreca, F.; Laudari, L.; Fichera, C.R. Using Landsat 8 imagery in detecting cork oak (Quercus suber L.) woodlands: A case study in Calabria (Italy). J. Agric. Eng. 2016, 47, 205-215
2. San-Miguel-Ayanz, J.; de Rigo, D.; Caudullo, G.; Houston Durrant, T.; Mauri, A. (Eds.) European Forest Tree Species; Publication Office of the European Union: Luxembourg, 2016
3. Pádua, L.; Vanko, J.; Hruška, J.; Adão, T.; Sousa, J.J.; Peres, E.; Morais, R. UAS, sensors, and data processing in agroforestry: A review towards practical applications. Int. J. Remote Sens. 2017, 38, 2349-2391
4. Michez, A.; Piégay, H.; Lisein, J.; Claessens, H.; Lejeune, P. Classification of riparian forest species and health condition using multi-temporal and hyperspatial imagery from unmanned aerial system. Environ. Monit. Assess. 2016, 188, 1-19
5. Böhler, J.E.; Schaepman, M.E.; Kneubühler, M. Crop classification in a heterogeneous arable landscape using uncalibrated UAV data. Remote Sens. 2018, 10, 1282
6. De Castro, A.I.; Torres-Sánchez, J.; Peña, J.M.; Jiménez-Brenes, F.M.; Csillik, O.; López-Granados, F. An automatic random forest-OBIA algorithm for early weed mapping between and within crop rows using UAV imagery. Remote Sens. 2018, 10, 285
7. Yuan, C.; Zhang, Y.; Liu, Z. A survey on technologies for automatic forest fire monitoring, detection, and fighting using unmanned aerial vehicles and remote sensing techniques. Can. J. For. Res. 2015, 45, 783-792
8. Lehmann, J.R.K.; Nieberding, F.; Prinz, T.; Knoth, C. Analysis of unmanned aerial system-based CIR images in forestry-a new perspective to monitor pest infestation levels. Forests 2015, 6, 594-612
9. Candiago, S.; Remondino, F.; De Giglio, M.; Dubbini, M.; Gattelli, M. Evaluating multispectral images and vegetation indices for precision farming applications from UAV images. Remote Sens. 2015, 7, 4026-4047
10. Lisein, J.; Pierrot-Deseilligny, M.; Bonnet, S.; Lejeune, P. A photogrammetric workflow for the creation of a forest canopy height model from small unmanned aerial system imagery. Forests 2013, 4, 922-944
11. Guerra-Hernández, J.; González-Ferreiro, E.; Sarmento, A.; Silva, J.; Nunes, A.; Correia, A.C.; Fontes, L.; Tomé, M.; Díaz-Varela, R. Using high resolution UAV imagery to estimate tree variables in Pinus pinea plantation in Portugal. For. Syst. 2016, 25
12. Gini, R.; Passoni, D.; Pinto, L.; Sona, G. Use of unmanned aerial systems for multispectral survey and tree classification: A test in a park area of northern Italy. Eur. J. Remote Sens. 2014, 47, 251-269
13. Haest, B.; Borre, J.; Vanden Spanhove, T.; Thoonen, G.; Delalieux, S.; Kooistra, L.; Mücher, C.A.; Paelinckx, D.; Scheunders, P.; Kempeneers, P. Habitat mapping and quality assessment of NATURA 2000 heathland using airborne imaging spectroscopy. Remote Sens. 2017, 9, 266
14. Goodbody, T.R.; Coops, N.C.; Hermosilla, T.; Tompalski, P.; Crawford, P. Assessing the status of forest regeneration using digital aerial photogrammetry and unmanned aerial systems. Int. J. Remote Sens. 2018, 39, 5246-5264
15. Van Iersel, W.; Straatsma, M.; Middelkoop, H.; Addink, E.; van Iersel, W.; Straatsma, M.; Middelkoop, H.; Addink, E. Multitemporal Classification of River Floodplain Vegetation Using Time Series of UAV Images. Remote Sens. 2018, 10, 1144
16. Tang, L.; Shao, G. Drone remote sensing for forestry research and practices. J. For. Res. 2015, 26, 791-797
17. Pádua, L.; Hruška, J.; Bessa, J.; Adão, T.; Martins, L.M.; Gonçalves, J.A.; Peres, E.; Sousa, A.M.R.; Castro, J.P.; Sousa, J.J. Multi-temporal analysis of forestry and coastal environments using UASs. Remote Sens. 2018, 10
18. Torresan, C.; Berton, A.; Carotenuto, F.; di Gennaro, S.F.; Gioli, B.; Matese, A.; Miglietta, F.; Vagnoli, C.; Zaldei, A.; Wallace, L. Forestry applications of UAVs in Europe: A review. Int. J. Remote Sens. 2016, 38, 1-21
19. Torres-Sánchez, J.; López-Granados, F.; Peña, J.M. An automatic object-based method for optimal thresholding in UAV images: Application for vegetation detection in herbaceous crops. Comput. Electron. Agric. 2015, 114, 43-52
20. Teodoro, A.C.; Araujo, R. Comparison of performance of object-based image analysis techniques available in open source software (Spring and Orfeo Toolbox/Monteverdi) considering very high spatial resolution data. J. Appl. Remote Sens. 2016, 10, 016011
21. Torres-Sánchez, J.; López-Granados, F.; de Castro, A.I.; Peña-Barragán, J.M. Configuration and Specifications of an Unmanned Aerial Vehicle (UAV) for Early Site Specific Weed Management. PLoS ONE 2013, 8, e58210
22. Goodbody, T.R.H.; Coops, N.C.; Marshall, P.L.; Tompalski, P.; Crawford, P. Unmanned aerial systems for precision forest inventory purposes: A review and case study. For. Chron. 2017, 93, 71-81
23. Robertson, L.D.; King, D.J. Comparison of pixel-and object-based classification in land cover change mapping. Int. J. Remote Sens. 2011, 32, 1505-1529
24. Liu, D.; Xia, F. Assessing object-based classification: Advantages and limitations. Remote Sens. Lett. 2010, 1, 187-194
25. Ma, L.; Li, M.; Ma, X.; Cheng, L.; Du, P.; Liu, Y. A review of supervised object-based land-cover image classification. ISPRS J. Photogramm. Remote Sens. 2017, 130, 277-293
26. Phiri, D.; Morgenroth, J. Developments in Landsat Land Cover Classification Methods: AReview. Remote Sens. 2017, 9, 967
27. Blaschke, T.; Hay, G.J.; Kelly, M.; Lang, S.; Hofmann, P.; Addink, E.; Queiroz Feitosa, R.; van der Meer, F.; van der Werff, H.; van Coillie, F.; et al. Geographic Object-Based Image Analysis-Towards a new paradigm. ISPRS J. Photogramm. Remote Sens. 2014, 87, 180-191
28. Blaschke, T. Object based image analysis for remote sensing. ISPRS J. Photogramm. Remote Sens. 2010, 65, 2-16
29. Hay, G.J.; Castilla, G. Geographic Object-Based Image Analysis (GEOBIA): A new name for a new discipline. In Object-Based Image Analysis. Spatial Concepts for Knowledge-Driven Remote Sensing Applications; Blaschke, T., Lang, S., Hay, G.J., Eds.; Springer: Berlin/Heidelberg, Germany, 2008; pp. 75-89
30. Hay, G.J.; Blaschke, T. Special issue: Geographic object-based image analysis (GEOBIA). Photogramm. Eng. Remote Sensing 2010, 76, 121-122
31. Franklin, S.E.; Ahmed, O.S. Deciduous tree species classification using object-based analysis and machine learning with unmanned aerial vehicle multispectral data. Int. J. Remote Sens. 2018, 39, 5236-5245
32. Ahmed, O.S.; Shemrock, A.; Chabot, D.; Dillon, C.; Williams, G.; Wasson, R.; Franklin, S.E. Hierarchical land cover and vegetation classification using multispectral data acquired from an unmanned aerial vehicle. Int. J. Remote Sens. 2017, 38, 2037-2052
33. CNES OTB Development Team. Software Guide; CNES: Paris, France, 2018; pp. 1-814
34. OTB Development Team. OTB CookBook Documentation; CNES: Paris, France, 2018; p. 305
35. Cerasoli, S.; Costa e Silva, F.; Silva, J.M.N. Temporal dynamics of spectral bioindicators evidence biological and ecological differences among functional types in a cork oak open woodland. Int. J. Biometeorol. 2016, 60, 813-825
36. Costa-e-Silva, F.; Correia, A.C.; Piayda, A.; Dubbert, M.; Rebmann, C.; Cuntz, M.; Werner, C.; David, J.S.; Pereira, J.S. Effects of an extremely dry winter on net ecosystem carbon exchange and tree phenology at a cork oak woodland. Agric. For. Meteorol. 2015, 204, 48-57
37. Kljun, N.; Calanca, P.; Rotach, M.W.; Schmid, H.P. A simple two-dimensional parameterisation for Flux Footprint Prediction (FFP). Geosci. Model Dev. 2015, 8, 3695-3713
38. Soares, P.; Firmino, P.; Tomé, M.; Campagnolo, M.; Oliveira, J.; Oliveira, B.; Araújo, J. A utilização de Veículos Aéreos Não Tripulados no inventário florestal-o caso do montado de sobro. In Proceedings of the VII Conferência Nac. Cartogr. e Geod., Lisbon, Portugal, 29-30 October 2015
39. Cresson, R.; Grizonnet, M.; Michel, J. Orfeo ToolBox Applications. In QGIS and Generic Tools; JohnWiley & Sons, Inc.: Hoboken, NJ, USA, 2018; pp. 151-242
40. Grizonnet, M.; Michel, J.; Poughon, V.; Inglada, J.; Savinaud, M.; Cresson, R. Orfeo ToolBox: Open source processing of remote sensing images. Open Geospatial Data Softw. Stand. 2017, 2, 15
41. Immitzer, M.; Vuolo, F.; Atzberger, C. First experience with Sentinel-2 data for crop and tree species classifications in central Europe. Remote Sens. 2016, 8, 166
42. Michel, J.; Youssefi, D.; Grizonnet, M. Stable Mean-Shift Algorithm and Its Application to the Segmentation of Arbitrarily Large Remote Sensing Images. IEEE Trans. Geosci. Remote Sens. 2015, 53, 952-964
43. Li, M.; Ma, L.; Blaschke, T.; Cheng, L.; Tiede, D.Asystematic comparison of different object-based classification techniques using high spatial resolution imagery in agricultural environments. Int. J. Appl. Earth Obs. Geoinf. 2016, 49, 87-98
44. Costa, H.; Foody, G.M.; Boyd, D.S. Supervised methods of image segmentation accuracy assessment in land cover mapping. Remote Sens. Environ. 2018, 205, 338-351
45. Belgiu, M.; Dragut, L. Comparing supervised and unsupervised multiresolution segmentation approaches for extracting buildings from very high resolution imagery. ISPRS J. Photogramm. Remote Sens. 2014, 96, 67-75
46. Congalton, R.G.; Green, K. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices; Second CRC Press Taylor & Francis Group: Boca Raton, FL, USA, 2009; Volume 48
47. Cortes, C.; Vapnik, V. Support-Vector Networks Editor. Mach. Learn. 1995, 20, 273-297
48. Vapnik, V. Statistical Learning Theory;Wiley and sons: New York, NY, USA, 1998
49. Mountrakis, G.; Im, J.; Ogole, C. Support vector machines in remote sensing: A review. ISPRS J. Photogramm. Remote Sens. 2011, 66, 247-259
50. Adam, E.; Mutanga, O.; Odindi, J.; Abdel-Rahman, E.M. Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: Evaluating the performance of random forest and support vector machines classifiers. Int. J. Remote Sens. 2014, 35, 3440-3458
51. Hawrylo, P.; Bednarz, B.;Wezyk, P.; Szostak, M. Estimating defoliation of Scots pine stands using machine learning methods and vegetation indices of Sentinel-2. Eur. J. Remote Sens. 2018, 51, 194-204
52. Wessel, M.; Brandmeier, M.; Tiede, D.;Wessel, M.; Brandmeier, M.; Tiede, D. Evaluation of Different Machine Learning Algorithms for Scalable Classification of Tree Types and Tree Species Based on Sentinel-2 Data. Remote Sens. 2018, 10, 1419
53. Breiman, L. Random forests. Mach. Learn. 2001, 45, 5-32
54. Cutler, D.R.; Edwards, T.C.; Beard, K.H.; Cutler, A.; Hess, K.T.; Gibson, J.; Lawler, J.J. Random Forests For Classification In Ecology. Ecology 2007, 88, 2783-2792
55. Trisasongko, B.H.; Panuju, D.R.; Paull, D.J.; Jia, X.; Griffin, A.L. Comparing six pixel-wise classifiers for tropical rural land cover mapping using four forms of fully polarimetric SAR data. Int. J. Remote Sens. 2017, 38, 3274-3293
56. Kaufman, Y.J.; Remer, L.A. Detection of Forests Using Mid-IR Reflectance: An Application for Aerosol Studies. IEEE Trans. Geosci. Remote Sens. 1994, 32, 672-683
57. Dragut, L.; Csillik, O.; Eisank, C.; Tiede, D. Automated parameterisation for multi-scale image segmentation on multiple layers. ISPRS J. Photogramm. Remote Sens. 2014, 88, 119-127
58. Clinton, N.; Holt, A.; Scarborough, J.; Yan, L.; Gong, P. Accuracy Assessment Measures for Object-based Image Segmentation Goodness. Photogramm. Eng. Remote Sens. 2010, 76, 289-299
59. Prošek, J.; Šímová, P.UAV for mapping shrubland vegetation: Does fusion of spectral and vertical information derived from a single sensor increase the classification accuracy? Int. J. Appl. Earth Obs. Geoinf. 2019, 75, 151-162
60. Díaz-Varela, R.A.; Calvo Iglesias, S.; Cillero Castro, C.; Díaz Varela, E.R. Sub-metric analisis of vegetation structure in bog-heathland mosaics using very high resolution rpas imagery. Ecol. Indic. 2018, 89, 861-873
61. Marpu, P.R.; Neubert, M.; Herold, H.; Niemeyer, I. Enhanced evaluation of image segmentation results. J. Spat. Sci. 2010, 55, 55-68
62. Gao, Y.; Mas, J.F.; Kerle, N.; Navarrete Pacheco, J.A. Optimal region growing segmentation and its effect on classification accuracy. Int. J. Remote Sens. 2011, 32, 3747-3763
63. Yu, Q.;Gong, P.; Clinton, N.; Biging, G.; Kelly, M.; Schirokauer, D.Object-basedDetailedVegetationClassification with Airborne High Spatial Resolution Remote Sensing Imagery. Photogramm. Eng. Remote Sens. 2006, 72, 799-811